随着机器学习、深度学习与人工智能的兴起与发展，自然语言处理技术也不断革新，其中实体抽取作为自然语言处理的子任务，自其提出以来，实现了从基于匹配模式的抽取到基于传统的机器学习方法抽取，再到基于深度学习的抽取的进步与发展。在医疗信息抽取方面，自然语言处理技术也得到了很好地应用，医学信息得到了更深层次的处理和挖掘。面对中医药领域中灵活的、复杂的和海量的中医药信息，利用实体抽取技术可以将中医药领域更为重要的信息抽取，这也是进一步对中医药信息挖掘的关键一步，基础一步。本文概括了中医药各领域实体抽取技术的应用研究情况，为实体抽取技术在中医药领域的进一步应用发展提供参考，以期促进中医药的传承发展，守正创新。

As machine learning, deep learning, and the emergence and development of artificial intelligence, natural language processing technology also constantly innovation, accordingly the entity extraction as child tasks of natural language processing, since its realized from extraction based on the pattern of matching to the machine learning method based on the traditional extraction, and based on the progress of the extraction of deep learning and development. In medical information extraction, natural language processing technology has also been well applied, and medical information has been processed and mined in a deeper level. In the face of flexible, complex and massive TCM information in the field of TCM, entity extraction technology can be used to extract more important information in the field of TCM, which is also a key and basic step for further TCM information mining. This paper summarizes the application and research of entity extraction technology in various fields of traditional Chinese medicine, providing reference for the further application and development of entity extraction technology in the field of traditional Chinese medicine, so as to promote the inheritance and development of traditional Chinese medicine and uphold innovation.

中国工程科技知识中心建设项目（CKCEST-2020-1-18）：中医学专业知识服务系统，负责人：黄璐琦、李宗友；中国中医科学院基本科研业务费自主选题项目（ZZ130303）：中医传承工作站的系统集成与探索应用，负责人：于琦；国家自然科学基金委员会面上项目（81873200）：基于深度学习的脾胃病关键诊疗要素发现与临床优化决策研究，负责人：李敬华。